Ultrasound beams propagating in biological tissues undergo distortions due to local inhomogeneities of the acoustic parameters and the nonlinearity of the medium. The spectral analysis of the radio-frequency (RF) backscattered signals may yield important clinical information in the field of tissue characterization, as well as enhancing the detectability of tissue parenchymal diseases. In this paper, we propose a new tissue spectral imaging technique based on the wavelet packets (WP) decomposition. In a conventional ultrasound imaging system, the received echo-signals are generally decimated to generate a medical image, with a loss of information. With the proposed approach, all the RF data are processed to generate a set of frequency subband images. The ultrasound echo signals are simultaneously frequency decomposed and decimated, by using two quadrature mirror filters, followed by a dyadic subsampling. In addition, to enhance the lesion detectability and the image quality, we apply a nonlinear filter to reduce noise in each subband image. The proposed method requires simple additional signal processing and it can be implemented on any real-time imaging system. The frequency subband images, which are available simultaneously, can be either used in a multispectral display or summed up together to reduce speckle noise. To localize the different frequency response in the tissues, we propose a multifrequency display method where three different subband images, chosen among those available, are encoded as red, green, and blue intensities (RGB) to create a false-colored RGB image. According to the clinical application, different choices can evidence different spectral proprieties in the biological tissue under investigation. To enhance the lesion contrast in a grey-level image, one of the possible methods is the summation of the images obtained from narrow frequency subbands, according to the frequency compounding technique. We show that by adding the denoised subband images created with the WP decomposition, the contrast-to-noise ratio in two phantom images is largely increased.
Purpose: To investigate the accuracy of various algorithms for deformable image registration (DIR), to propagate regions of interest (ROIs) in computational phantoms based on patient images using different commercial systems. This work is part of an Italian multi‐institutional study to test on common datasets the accuracy, reproducibility and safety of DIR applications in Adaptive Radiotherapy. Methods: Eleven institutions with three available commercial solutions provided data to assess the agreement of DIR‐propagated ROIs with automatically drown ROIs considered as ground‐truth for the comparison. The DIR algorithms were tested on real patient data from three different anatomical districts: head and neck, thorax and pelvis. For every dataset two specific Deformation Vector Fields (DVFs) provided by ImSimQA software were applied to the reference data set. Three different commercial software were used in this study: RayStation, Velocity and Mirada. The DIR‐mapped ROIs were then compared with the reference ROIs using the Jaccard Conformity Index (JCI). Results: More than 600 DIR‐mapped ROIs were analyzed. Putting together all JCI data of all institutions for the first DVF, the mean JCI was 0.87 ± 0.7 (1 SD) while for the second DVF JCI was 0.8 ± 0.13 (1 SD). Several considerations on different structures are available from collected data: the standard deviation among different institutions on specific structure raise as the larger is the applied DVF. The higher value is 10% for bladder. Conclusion: Although the complexity of deformation of human body is very difficult to model, this work illustrates some clinical scenarios with well‐known DVFs provided by specific software. CI parameter gives the inter‐user variability and may put in evidence the need of improving the working protocol in order to reduce the inter‐institution JCI variability.
In this paper, errors and discrepancies in the subject paper [Cincotti et al., (2002)] are highlighted. A comment, concerning the axial resolution associated to the adopted processing procedure is also reported.
In this section, simulated results are presented to quantify the temporal shift induced by the nonlinear phase of the Daubechies filter family over the DWPT coefficient set calculated for different subbands. A 5-MHz central frequency RF echo signal with 40-MHz sampling frequency was considered. The results produced by using four-level decomposition with Daubechies 3 (Db3) and Daubechies 9 (Db9) are reported in Figs. 3 and 4, respectively. The four -level decomposition produces 16 subbands but, for sake of brevity, only eight of them are represented.The relative temporal shifts between the coefficient set calculated for different subbands induce a spread in the interface localization if no inverse reconstruction algorithm is applied.Moreover, it must be noted that the temporal shift is proportional to the Daubechies order, in fact Db9 exhibits larger temporal shift between the subband coefficient set, with respect to Db3 as it appears by comparing Fig. 3 with Fig. 4. C. Loss of Resolution by DWPT Decomposition With Daubechies Family Without Inverse DWPTThe temporal shifts associate to DWPT decomposition with Daubechies family do not permit the direct composition of the subband images; an inverse DWPT is necessary to avoid the final image resolution loss as appears in [1, Figs. 6 and 7].In Fig. 5 is reported the direct composition, following the procedure reported in [1, Fig. 5] of the 16 subband images obtained through four-level Db3 and Db9. Denoising is not applied to our data while the "equalization" step is substituted by a normalization procedure because no details are given about the adopted "equalization" function. The loss of resolution appears evident by comparing these images with the original B-Mode in Fig. 1.As an obvious proof in Fig. 6, the original B-Mode image and the image obtained through an inverse DWPT are reported. It can be seen that no difference exist between the two images when the Inverse DWPT is applied as the correct procedure requires. REFERENCES[1] G. Cincotti, G. Loi, and M. Pappalardo, "Frequency decomposition and compounding of ultrasound medical images with wavelet packets," ] "Method for enhancing the diagnostic power of ultrasonic scanning system by using real time spectral maps, and device operating by said method," US Patent 6 066 098, May 23, 2000. (a) (b) Fig. 5. (a) Sum of the envelope of the normalized DWPT four-level decomposition coefficients through Db3 filter. (b) Sum of the envelope of the normalized DWPT four-level decomposition coefficients through Db9 filter. The loss of resolution appears more evident with Db9 with respect to Db3. (a) (b) Fig. 6. (a) Original B-mode. (b) B-mode obtained through the inverse DWPT of four-level decomposition with Db3 filter.
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